Individualized patient risk stratification requires machine learning models to predict the future disease state of a patient based on his or her current clinical state and available history 4. One of the applications with the widest potential is patient risk stratification (i.e., diagnosis, prognosis) 3.

Machine learning for healthcare promises to have a major impact on the delivery of data-driven personalized medicine 1, 2. For models to guide clinicians in individual decisions, performance exceeding these benchmarks is necessary. These results provide a benchmark for predictive accuracy trained only on prior clinical actions and indicate that models with similar performance may derive their signal by looking over clinician’s shoulders-using clinical behavior as the expression of preexisting intuition and suspicion to generate a prediction. Furthermore, these models exhibited significant declines in performance when evaluated over only myocardial infarction (MI) patients relative to models trained over MI patients alone, highlighting the importance of physician diagnosis in the prognostic performance of these models. Similar performance between models trained with only clinician-initiated data and those trained with full EMR data purporting to include information about patient state and physiology should raise concern in the deployment of these models. Models trained on charges during the first day of admission achieve performance close to published full EMR-based benchmarks for inpatient outcomes: inhospital mortality (0.89 AUC), prolonged length of stay (0.82 AUC), and 30-day readmission rate (0.71 AUC).

We trained deep learning models using only clinician-initiated, administrative data for 42.9 million admissions using three subsets of data: demographic data only, demographic data and information available at admission, and the previous data plus charges recorded during the first day of admission. Machine learning can help clinicians to make individualized patient predictions only if researchers demonstrate models that contribute novel insights, rather than learning the most likely next step in a set of actions a clinician will take.

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